How Do AI Climate Models Respond to Warming Across Climate Zones?
(2026)
New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models
Earth Syst. Dynam., 17, 1061–1079, 2026
Abstract:
The Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation (NAO) are the dom-
inant modes of oceanic and atmospheric variability in the North Atlantic, respectively, and are key sources of
predictability from seasonal to decadal timescales. However, the physical processes and feedback mechanisms
linking the AMV and NAO, and the role of diabatic processes in these feedbacks, remain debated. We present
a data-driven dynamical modelling framework which captures coupled decadal variability in AMV, NAO, and
North Atlantic precipitation. Applying equation discovery methods to observational data, we identify low-order
models consisting of three coupled ordinary differential equations. These models reproduce observed decadal
variability and show robust out-of-sample predictive skill on multi-annual to decadal lead times. The resulting
model dynamics include a distinct quasi-periodic 20-year oscillation consistent with a damped oceanic mode
of variability. Notably, precipitation-related terms feature prominently in the low-order models, suggesting an
important role for latent heat release and freshwater fluxes in mediating ocean–atmosphere interactions. We pro-
pose new feedback mechanisms between North Atlantic sea surface temperature and the NAO, with precipitation
acting as a dynamical bridge. Overall, these results illustrate how equation discovery can provide mechanistic
hypotheses and new insight beyond conventional analyses of observations and climate model simulations.
inant modes of oceanic and atmospheric variability in the North Atlantic, respectively, and are key sources of
predictability from seasonal to decadal timescales. However, the physical processes and feedback mechanisms
linking the AMV and NAO, and the role of diabatic processes in these feedbacks, remain debated. We present
a data-driven dynamical modelling framework which captures coupled decadal variability in AMV, NAO, and
North Atlantic precipitation. Applying equation discovery methods to observational data, we identify low-order
models consisting of three coupled ordinary differential equations. These models reproduce observed decadal
variability and show robust out-of-sample predictive skill on multi-annual to decadal lead times. The resulting
model dynamics include a distinct quasi-periodic 20-year oscillation consistent with a damped oceanic mode
of variability. Notably, precipitation-related terms feature prominently in the low-order models, suggesting an
important role for latent heat release and freshwater fluxes in mediating ocean–atmosphere interactions. We pro-
pose new feedback mechanisms between North Atlantic sea surface temperature and the NAO, with precipitation
acting as a dynamical bridge. Overall, these results illustrate how equation discovery can provide mechanistic
hypotheses and new insight beyond conventional analyses of observations and climate model simulations.
New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models
Earth System Dynamics Copernicus Publications 17:4 (2026) 1061-1079
Abstract:
Abstract. The Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation (NAO) are the dominant modes of oceanic and atmospheric variability in the North Atlantic, respectively, and are key sources of predictability from seasonal to decadal timescales. However, the physical processes and feedback mechanisms linking the AMV and NAO, and the role of diabatic processes in these feedbacks, remain debated. We present a data-driven dynamical modelling framework which captures coupled decadal variability in AMV, NAO, and North Atlantic precipitation. Applying equation discovery methods to observational data, we identify low-order models consisting of three coupled ordinary differential equations. These models reproduce observed decadal variability and show robust out-of-sample predictive skill on multi-annual to decadal lead times. The resulting model dynamics include a distinct quasi-periodic 20-year oscillation consistent with a damped oceanic mode of variability. Notably, precipitation-related terms feature prominently in the low-order models, suggesting an important role for latent heat release and freshwater fluxes in mediating ocean–atmosphere interactions. We propose new feedback mechanisms between North Atlantic sea surface temperature and the NAO, with precipitation acting as a dynamical bridge. Overall, these results illustrate how equation discovery can provide mechanistic hypotheses and new insight beyond conventional analyses of observations and climate model simulations.Spatial Generalization Tests for Machine Learning-based Weather Models to Assess Physical Consistency
(2026)
Shipping and fishing vessels as sources of marine plastic debris for the Seychelles
Marine Pollution Bulletin Elsevier 233:Pt 1 (2026) 120064